Reducing hvac loads for rideshare vehicles

ABSTRACT

Systems and methods provide for reducing power draw in an autonomous vehicle. A level of sunlight can be received based on an amount of light captured by one or more sensors. The level of sunlight can be mapped to a geographical area to generate a map of shaded areas, and based on a temperature within the autonomous vehicle being outside a temperature range, the autonomous vehicle can be routed based on the map of shaded areas to bring the autonomous vehicle within the temperature range.

TECHNICAL FIELD

The subject matter of this disclosure relates in general to the field ofrideshare vehicles, and more particularly, to systems and methods fortemperature management in an autonomous rideshare vehicle.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can navigate without ahuman driver. An exemplary autonomous vehicle includes a plurality ofsensor systems, such as, but not limited to, a camera sensor system, alidar sensor system, a radar sensor system, amongst others, wherein theautonomous vehicle operates based upon sensor signals output by thesensor systems. Specifically, the sensor signals are provided to aninternal computing system in communication with the plurality of sensorsystems, wherein a processor executes instructions based upon the sensorsignals to control a mechanical system of the autonomous vehicle, suchas a vehicle propulsion system, a braking system, or a steering system.

Autonomous vehicles can be operated within a fleet of vehicles. Sinceautonomous vehicles can be electric, requiring the autonomous vehiclesto be charged, any kind of charging infrastructure could be benefited bysaving power draw during the autonomous vehicle's operation. Reducingpower draw, such as power devoted to heating and air conditioningsystems, can have cascading effects across the entire fleet.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-recited and other advantages and features of the presenttechnology will become apparent by reference to specific implementationsillustrated in the appended drawings. A person of ordinary skill in theart will understand that these drawings only show some examples of thepresent technology and would not limit the scope of the presenttechnology to these examples. Furthermore, the skilled artisan willappreciate the principles of the present technology as described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates an example schematic diagram of an autonomous vehicleand network environment, in accordance with some embodiments;

FIG. 2 illustrates an example schematic diagram of an autonomous vehicleand network environment that reduces power draw, in accordance with someembodiments;

FIG. 3 illustrates a flowchart representation of reducing power draw ofan autonomous vehicle, in accordance with some embodiments; and

FIG. 4 shows an example of a system for implementing certain aspects ofthe present technology.

DETAILED DESCRIPTION

Various examples of the present technology are discussed in detailbelow. While specific implementations are discussed, it should beunderstood that this is done for illustration purposes only. A personskilled in the relevant art will recognize that other components andconfigurations may be used without parting from the spirit and scope ofthe present technology. In some instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing one or more aspects. Further, it is to be understood thatfunctionality that is described as being carried out by certain systemcomponents may be performed by more or fewer components than shown.

The disclosed technology addresses the need in the art for reducing thecosts of operating autonomous vehicles. A large use of power is heating,ventilation, and air conditioning (HVAC) systems. For example, onelectric vehicles, especially in cold climates, the heater can consumelarge amounts of power because there's no internal combustion engine totake the waste heat from. In some cases, depending on the outsideclimate, up to a third of total power from the battery can be a resultof the HVAC system. What is needed to save power is a way to reduce theoperation of HVAC systems.

FIG. 1 illustrates environment 100 that includes an autonomous vehicle102 in communication with a remote computing system 150.

The autonomous vehicle 102 can navigate about roadways without a humandriver based upon sensor signals output by sensor systems 104-106 of theautonomous vehicle 102. The autonomous vehicle 102 includes a pluralityof sensor systems 104-106 (a first sensor system 104 through an Nthsensor system 106). The sensor systems 104-106 are of different typesand are arranged about the autonomous vehicle 102. For example, thefirst sensor system 104 may be a camera sensor system and the Nth sensorsystem 106 may be a lidar sensor system. Other exemplary sensor systemsinclude radar sensor systems, global positioning system (GPS) sensorsystems, inertial measurement units (IMU), infrared sensor systems,laser sensor systems, sonar sensor systems, and the like.

The autonomous vehicle 102 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle102. For instance, the mechanical systems can include but are notlimited to, a vehicle propulsion system 130, a braking system 132, and asteering system 134. The vehicle propulsion system 130 may include anelectric motor, an internal combustion engine, or both. The brakingsystem 132 can include an engine brake, brake pads, actuators, and/orany other suitable componentry that is configured to assist indecelerating the autonomous vehicle 102. The steering system 134includes suitable componentry that is configured to control thedirection of movement of the autonomous vehicle 102 during navigation.

The autonomous vehicle 102 further includes a safety system 136 that caninclude various lights and signal indicators, parking brake, airbags,etc. The autonomous vehicle 102 further includes a cabin system 138 thatcan include cabin temperature control systems, in-cabin entertainmentsystems, etc.

The autonomous vehicle 102 additionally comprises an autonomous vehicle(AV) internal computing system 110 that is in communication with thesensor systems 104-106 and the mechanical systems 130, 132, 134. The AVinternal computing system includes at least one processor and at leastone memory having computer-executable instructions that are executed bythe processor. The computer-executable instructions can make up one ormore services responsible for controlling the autonomous vehicle 102,communicating with remote computing system 150, receiving inputs frompassengers or human co-pilots, logging metrics regarding data collectedby sensor systems 104-106 and human co-pilots, etc.

The AV internal computing system 110 can include a control service 112that is configured to control operation of the vehicle propulsion system130, the braking system 132, the steering system 134, the safety system136, and the cabin system 138. The control service 112 receives sensorsignals from the sensor systems 104-106 as well communicates with otherservices of the AV internal computing system 110 to effectuate operationof the autonomous vehicle 102. In some embodiments, control service 112may carry out operations in concert with one or more other systems ofautonomous vehicle 102.

The AV internal computing system 110 can also include a constraintservice 114 to facilitate safe propulsion of the autonomous vehicle 102.The constraint service 114 includes instructions for activating aconstraint based on a rule-based restriction upon operation of theautonomous vehicle 102. For example, the constraint may be a restrictionupon navigation that is activated in accordance with protocolsconfigured to avoid occupying the same space as other objects, abide bytraffic laws, circumvent avoidance areas, etc. In some embodiments, theconstraint service can be part of the control service 112.

The AV internal computing system 110 can also include a communicationservice 116. The communication service 116 can include both software andhardware elements for transmitting and receiving signals from/to theremote computing system 150. The communication service 116 is configuredto transmit information wirelessly over a network, for example, throughan antenna array that provides personal cellular (long-term evolution(LTE), 3G, 5G, etc.) communication.

In some embodiments, one or more services of the AV internal computingsystem 110 are configured to send and receive communications to remotecomputing system 150 for such reasons as reporting data for training andevaluating machine learning algorithms, requesting assistance fromremote computing system 150 or a human operator via remote computingsystem 150, software service updates, ridesharing pickup and drop offinstructions, etc.

The AV internal computing system 110 can also include a latency service118. The latency service 118 can utilize timestamps on communications toand from the remote computing system 150 to determine if a communicationhas been received from the remote computing system 150 in time to beuseful. For example, when a service of the AV internal computing system110 requests feedback from remote computing system 150 on atime-sensitive process, the latency service 118 can determine if aresponse was timely received from remote computing system 150 asinformation can quickly become too stale to be actionable. When thelatency service 118 determines that a response has not been receivedwithin a threshold, the latency service 118 can enable other systems ofautonomous vehicle 102 or a passenger to make necessary decisions or toprovide the needed feedback.

The AV internal computing system 110 can also include a user interfaceservice 120 that can communicate with cabin system 138 in order toprovide information or receive information to a human co-pilot or humanpassenger. In some embodiments, a human co-pilot or human passenger maybe required to evaluate and override a constraint from constraintservice 114, or the human co-pilot or human passenger may wish toprovide an instruction to the autonomous vehicle 102 regardingdestinations, requested routes, or other requested operations.

As described above, the remote computing system 150 is configured tosend/receive a signal from the autonomous vehicle 102 regardingreporting data for training and evaluating machine learning algorithms,requesting assistance from remote computing system 150 or a humanoperator via the remote computing system 150, software service updates,ridesharing pickup and drop off instructions, etc.

The remote computing system 150 includes an analysis service 152 that isconfigured to receive data from autonomous vehicle 102 and analyze thedata to train or evaluate machine learning algorithms for operating theautonomous vehicle 102. The analysis service 152 can also performanalysis pertaining to data associated with one or more errors orconstraints reported by autonomous vehicle 102.

The remote computing system 150 can also include a user interfaceservice 154 configured to present metrics, video, pictures, soundsreported from the autonomous vehicle 102 to an operator of remotecomputing system 150. User interface service 154 can further receiveinput instructions from an operator that can be sent to the autonomousvehicle 102.

The remote computing system 150 can also include an instruction service156 for sending instructions regarding the operation of the autonomousvehicle 102. For example, in response to an output of the analysisservice 152 or user interface service 154, instructions service 156 canprepare instructions to one or more services of the autonomous vehicle102 or a co-pilot or passenger of the autonomous vehicle 102.

The remote computing system 150 can also include a rideshare service 158configured to interact with ridesharing applications 170 operating on(potential) passenger computing devices. The rideshare service 158 canreceive requests to be picked up or dropped off from passengerridesharing application 170 and can dispatch autonomous vehicle 102 forthe trip. The rideshare service 158 can also act as an intermediarybetween the ridesharing application 170 and the autonomous vehicle 102wherein a passenger might provide instructions to the autonomous vehicle102 to go around an obstacle, change routes, honk the horn, etc.

FIG. 2 illustrates an example schematic diagram of an autonomous vehicle202 and network environment that reduces power draw for interiortemperature management, such as reducing power to a heating,ventilation, and air conditioning (HVAC) system 230, in accordance withsome embodiments. System 200 can, for example, reduce HVAC system 230power draw in autonomous vehicle 202 by receiving a level of sunlightbased on an amount of light captured by one or more sensors. The levelof sunlight can be mapped to a geographical area to generate a map ofshaded areas and, based on a temperature within autonomous vehicle 202being outside a temperature range, the autonomous vehicle 202 can berouted based on the map of shaded areas to bring the autonomous vehicle202 back within the temperature range. This reduces the need to turn onan HVAC system 230 for thermal comfort within autonomous vehicle 202,thus saving power since the sun (or lack of it) can heat or cool theinterior of autonomous vehicle 202 instead of HVAC system 230,respectively.

System 200, for example, can include autonomous vehicle 202 incommunication with network 232, which can provide routing services forautonomous vehicle 202. Autonomous vehicle 202 can be part of fleet 226that includes any number of autonomous vehicles. Temperature can bemanaged within the interior of the autonomous vehicles by routingautonomous vehicles through (or to) shady areas when the autonomousvehicles need to cool down, and/or routing autonomous vehicles through(or to) sunny areas when the autonomous vehicles need to warm up. Insome embodiments, autonomous vehicle 202 can detect the level of light(e.g., irradiance, radiant energy, radiant flux, intensity, etc.) thatis incident on autonomous vehicle 202. At the same time, autonomousvehicle 202 can detect temperature within autonomous vehicle 202 as itis operating. This data can be collected and mapped to specificgeographical locations, which can then inform routes autonomous vehicle202 can take in order to maintain a consistent temperature range.Elements of the autonomous vehicle 202, the network 232, and the fleet226 will be discussed in greater detail with respect to the variousmethod steps in FIG. 3.

FIG. 3 illustrates a flowchart representation of reducing power draw ofan autonomous vehicle, in accordance with some embodiments. Method 300describes a process for constructing a map of shady/non-shady areas thatcan begin by receiving, by network 232, a level of sunlight based on anamount of light captured by one or more sensors (step 310). For example,autonomous vehicle 202 can include one or more sensors, such asphotoreceiver sensor(s) 204, which can detect ambient light conditions.The amount of light, such as the amount of sunlight falling through thewindshield of autonomous vehicle 202, can correlate with the amount ofnatural heat inside the autonomous vehicle. More light, for example, canindicate higher temperatures within autonomous vehicle 202 than lowerlight conditions.

In some embodiments, photoreceiver sensor(s) 204 can be locatedinternally and/or externally to autonomous vehicle 202, such as behind awindshield of the autonomous vehicle 202, mounted on the outside of theautonomous vehicle 202, etc. Moreover, cameras (not shown) can be usedto detect levels of some lighter shade and decouple whether lower lightlevels are due to sunlight levels or other reasons that can temporarilyconfuse the data (e.g., cloudy or foggy weather, passing pedestrians orpassing vehicles, parked trucks, etc.). In some embodiments, data fromthe photoreceiver sensor(s) 204 and/or cameras can be stored locally toautonomous vehicle 202 in data store 208.

In some embodiments, autonomous vehicle 202 can include temperaturesensor(s) 206, which can be located internally (e.g., the cabin ofautonomous vehicle 202) or externally. Temperature sensor(s) 206 candetect internal temperature conditions, such as the temperature withinthe cabin where passengers or transported goods are located. In someimplementations the temperature measured by temperature sensor(s) 206can be stored locally to autonomous vehicle 202 in data store 208. Thedata within data store 208 can in some embodiments be transmitted oruploaded to network 232. In some embodiments, a portion of analysis canbe done locally to autonomous vehicle 202 before being transmitted tonetwork 232. For example, internal analysis service 210 can, via thetemperature and light level data stored in data store 208, correlatelight levels to actual cabin temperature in an analysis to build a modeldescribing how temperature is affected by sunlight levels within thecabin.

Network 232 can receive data from the different types of sensors onautonomous vehicle 202 that detect temperature and light (e.g., datafrom data store 208), and then collect and aggregate that data. In someembodiments, the data stored on vehicle (e.g., data store 208) can besent over cellular data to network 232 on a periodic or continuousbasis. Another option could be to collect data on the vehicle (datastore 208), and instead of paying for cellular data, wait untilautonomous vehicle 202 gets back to a facility to transfer the data offvia WiFi to network 232.

In some embodiments, based on the temperature and light level data,network 232 can map measured levels of sunlight to a geographical area(step 312). For example, analysis service 216 can correlate thetemperature of autonomous vehicle interior (such as its cabin) tomeasured light levels according to one or more models within models 214.In one or more embodiments, the measured light levels can be furtherassociated with location data via location service 220, which canmonitor the current location of autonomous vehicle 202 by on board GPSsensors, to generate a map of shaded/non-shaded areas (step 314). Forexample, mapping service 222 can generate maps of shaded areas usingmodels that correlate the data from analysis service 216 and locationservice 220. The maps of shaded/non-shaded areas can be applied bysystem 200 to take advantage of routes that include areas with morelight or more shade in order to lower the cost of operating the fleet226 (e.g., by lowering power draw from the on board HVAC system 230 oneach autonomous vehicle).

For example, if autonomous vehicle 202 is left out in the sun, theinterior of the autonomous vehicle 202 can increase in temperaturewithout heat assistance from an HVAC system 230. The heat from the suncan be trapped within the interior of the autonomous vehicle 202,thereby acting as its own heating system. Conversely, if autonomousvehicle 202 is left in the shade (or drives in a shaded area or parkswithin a tall building's shadow), the autonomous vehicle 202 becomescooler. Autonomous vehicle 202 can then regulate its interiortemperature by seeking out shady areas when it gets too hot and sunnyareas when it gets too cold. For example, using photoreceiver sensor(s)204, usually located somewhere at the bottom of the windshield in thefront by the dashboard, autonomous vehicle 202 can detect how muchsunlight there is while the autonomous vehicle 202 is driving around acity as it normally would. Autonomous vehicle 202 could collect data asit is operating, as well as other autonomous vehicles within fleet 226,and then send that data back to network 232 to generate, modify, orrefine models 214. Over time, the fleet 226 of autonomous vehiclesoperating independently of each other, each driving around the city, cangenerate a detailed map of shaded areas.

In some embodiments, models 214 and/or the maps generated by mappingservice 222 can be continuously trained as fleet 226 drives throughoutthe city. If a threshold number of autonomous vehicles (or an averagenumber of autonomous vehicles) register higher or lower levels of lightthan expected from the maps, then models 214 and/or the maps can bemodified to reflect the actual measurements of shady vs. non-shadyareas.

In some embodiments, the maps can be supplemented with third partylocation information. For example, location service 220 can supplementits location information with third party maps or similar services,especially areas in which the fleet 226 does not or cannot drive within.These areas can be new parking facilities or specific properties thatthe fleet 226 may have been newly granted access.

Models 214 may in some embodiments take weather or environmentalconditions into account when generating models 214 and/or the maps frommapping service 222. Areas of shade can constantly change based on manyfactors, such as the position of the sun, buildings, pedestrians, orother obstacles within the city. Analysis service 216 can attributefluctuations in the level of sunlight to these factors, and can informmodels 214 so that the maps accurately reflect shady areas. In the caseof weather (such as cloudiness, rain, fog, etc. that reduces normalsunlight incidence), cameras located within and outside of the cabin canbe used in addition to the photoreceiver sensor(s) 204 to determine thatlower light levels are temporarily caused by the weather. In someembodiments, weather data from outside services, such as weather service218 (e.g., data from the National Oceanic and AtmosphericAdministration), can refine models 214 and mapping service 222.

Fluctuations in the level of sunlight can also reflect changes in thetime of day or time of year. For example, the way shadows move acrossthe city and throughout the day can vary throughout different periods ofthe year, as the sun moves higher or lower in the sky depending upon theseason. Mapping the level of sunlight to a geographical area can be donewith respect to these daily and yearly changes—e.g., with respect tovarying periods of time. Based on the period of time, a map of shadedareas that are based on a time of day and/or time of year as the sunchanges its position in the sky can be generated. In other words, theamount of light in a given location may be based on time, which may be apoint in time (e.g. 6:00 PM PST on January 1), a range of time (e.g. 11AM PST to 1 PM PST from April 1 to May 30), or other suitablemeasurement of time. Autonomous vehicle 202 can then be routed inaccordance with the map based on the current time.

Once the maps of shaded areas are generated, the autonomous vehicle 202can be routed according to the maps in order to maintain a desiredtemperature range within the cabin. Cabin temperature, then, can bemonitored (step 316) as the autonomous vehicle 202 operates or parks.For example, cabin temperature can be monitored via temperaturesensor(s) 206 located within the cabin of the autonomous vehicle 202.The temperature can be measured with respect to a desired temperaturerange at which the cabin of autonomous vehicle 202 should be kept. Ifthe monitored temperature is within the temperature range (step 318),then method 300 ends. However, if the temperature is outside thetemperature range (step 318), then the autonomous vehicle 202 can berouted such that the temperature is brought back within the desiredtemperature range. In some embodiments, an option for the autonomousvehicle 202 to skip a routing change can be taken, if the weather /sunlight would not improve the cabin temperature (this essentially is anoption to turn off the feature).

The desired temperature range could be comfortable for human habitationand/or an optimal range for goods that are being transported. In someembodiments the temperature range can depend on data relating tocustomer preferences. For example, customers can have a profile that caninform temperature settings when they get into the autonomous vehicle202. If in the past the customer has modified the temperature to bewarmer or cooler than the default temperature range, then that feedbackfrom the customer can be taken into account and the desired temperaturerange adjusted accordingly to customize the customer's experience. Insome embodiments, the customer profile can be developed through anapplication on a mobile device and/or through tablets in autonomousvehicle 202 that can include adjustable temperature controls when thecustomer is sitting in a seat within the autonomous vehicle 202.

The desired temperature range can be related to the delivery of goodsinstead of, or in addition to, human passenger comfort. For example,autonomous vehicle 202 can deliver pizza or other similarly warm, edibleproducts. Autonomous vehicle 202 can be driven in such a way thatpreserves the temperature of the deliverable good, such as high sunlightroutes for warm takeout or shadier routes for cooler desserts.

As a result, based on a temperature within the autonomous vehicle 202being outside the temperature range, autonomous vehicle 202 can berouted based on the map of shaded areas in order to bring autonomousvehicle 202 back within the desired temperature range. If thetemperature is below the temperature range, then autonomous vehicle 202can be routed to a location with a higher level of sunlight (step 320).For example, analysis service 216 can determine that a temperature of acabin of autonomous vehicle 202, as measured by temperature sensor(s)206, has fallen below a threshold temperature (e.g., the minimum of thetemperature range). Based on that determination, routing service 224 cangenerate a route to locations determined by mapping service 222 to havehigher levels of sunlight. Routing service 224 can then send instructionto control service 212 to operate autonomous vehicle 202 along thegenerated route in order to warm up the cabin.

Similarly, if the temperature is above the temperature range, thenautonomous vehicle 202 can be routed to a location with a lower level ofsunlight (step 322) in order to cool it down. For example, analysisservice 216 can determine that the temperature of the cabin ofautonomous vehicle 202, as measured by temperature sensor(s) 206, hasrisen above a threshold temperature (e.g., the maximum of thetemperature range) and needs to be lowered. Based on that determination,routing service 224 can generate a route to locations determined bymapping service 222 to have lower levels of sunlight (e.g., higherlevels of shade). Routing service 224 can then send instruction tocontrol service 212 to operate autonomous vehicle 202 along thegenerated route.

In some embodiments, routing service 224 can dispatch the fleet 226 ofautonomous vehicles using weighted algorithms, where keeping temperatureconsistent is one of many factors to be weighted in the generation ofeach route taken by an autonomous vehicle. Some additional factors canbe traffic congestion along certain routes, which could overridetemperature management concerns if the route takes too long to traverse.Other weighted factors can be high or low parking demands, speed of theroute, safety, etc. Moreover, in some embodiments if there are multiplelanes, such as four or five lanes to choose from on a given route, thenrouting service 224 can choose a particular lane versus the other lanesdepending on the amount of shade and temperature needs of the cabin.

In some embodiments, autonomous vehicle 202 can employ automatic shades234. For example, if the autonomous vehicle 202 is parked in a sunnyarea (as detected by the level of light detected by photoreceiversensor(s) 204), the automatic shades 234 can extend across thewindshield to decrease the amount of sunlight within the cabin.Conversely, the autonomous vehicle 202 can move to a sunnier area topark if photoreceiver sensor(s) 204 detects that it's in a shady area.In some embodiments, the automatic shades 234 can take the form of smartglass, darkening or lightening in response to light levels detected byphotoreceiver sensor(s) 204.

As described herein, one aspect of the present technology is thegathering and use of data available from various sources to improvequality and experience. The present disclosure contemplates that in someinstances, this gathered data may include personal information. Thepresent disclosure contemplates that the entities involved with suchpersonal information respect and value privacy policies and practices.

FIG. 4 shows an example of computing system 400, which can be forexample any computing device making up AV internal computing system 110,remote computing system 150, (potential) passenger device executingrideshare application 170, or any component thereof in which thecomponents of the system are in communication with each other usingconnection 405. Connection 405 can be a physical connection via a bus,or a direct connection into processor 410, such as in a chipsetarchitecture. Connection 405 can also be a virtual connection, networkedconnection, or logical connection.

In some embodiments, computing system 400 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple data centers, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 400 includes at least one processing unit (CPU orprocessor) 410 and connection 405 that couples various system componentsincluding system memory 415, such as read-only memory (ROM) 420 andrandom access memory (RAM) 425 to processor 410. Computing system 400can include a cache of high-speed memory 412 connected directly with, inclose proximity to, or integrated as part of processor 410.

Processor 410 can include any general purpose processor and a hardwareservice or software service, such as services 432, 434, and 436 storedin storage device 430, configured to control processor 410 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 410 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 400 includes an inputdevice 445, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 400 can also include output device 435, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 400.Computing system 400 can include communications interface 440, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement, andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Storage device 430 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs), read-only memory (ROM), and/or somecombination of these devices.

The storage device 430 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 410, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable medium inconnection with the necessary hardware components, such as processor410, connection 405, output device 435, etc., to carry out the function.

For clarity of explanation, in some instances, the present technologymay be presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

Any of the steps, operations, functions, or processes described hereinmay be performed or implemented by a combination of hardware andsoftware services or services, alone or in combination with otherdevices. In some embodiments, a service can be software that resides inmemory of a client device and/or one or more servers of a contentmanagement system and perform one or more functions when a processorexecutes the software associated with the service. In some embodiments,a service is a program or a collection of programs that carry out aspecific function. In some embodiments, a service can be considered aserver. The memory can be a non-transitory computer-readable medium.

In some embodiments, the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer-readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The executable computer instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, solid-state memory devices, flash memory, USB devices providedwith non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include servers,laptops, smartphones, small form factor personal computers, personaldigital assistants, and so on. The functionality described herein alsocan be embodied in peripherals or add-in cards. Such functionality canalso be implemented on a circuit board among different chips ordifferent processes executing in a single device, by way of furtherexample.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

What is claimed is:
 1. A method to reduce power draw in an autonomous vehicle, the method comprising: receiving a level of sunlight based on an amount of light captured by one or more sensors; mapping the level of sunlight to a geographical area to generate a map of shaded areas; and based on a temperature within the autonomous vehicle being outside a temperature range, routing the autonomous vehicle based on the map of shaded areas to bring the autonomous vehicle within the temperature range.
 2. The method of claim 1, wherein the one or more sensors are photo receiver sensors that detect ambient light conditions, and wherein the one or more sensors are located behind a windshield of the autonomous vehicle.
 3. The method of claim 1, wherein the map of shaded areas is generated based on mapping levels of sunlight received from a fleet of autonomous vehicles.
 4. The method of claim 1, wherein the mapping of the level of sunlight to the geographical area is with respect to time, and the routing of the autonomous vehicle is further based on a current time.
 5. The method of claim 1, wherein the temperature is received from a temperature sensor located within a cabin of the autonomous vehicle.
 6. The method of claim 1, further comprising: determining that a temperature of a cabin of the autonomous vehicle has fallen below a threshold temperature; and based on the determination, routing the autonomous vehicle to a location with a higher level of sunlight.
 7. The method of claim 1, further comprising: determining that a temperature of a cabin of the autonomous vehicle has risen above a threshold temperature; and based on the determination, routing the autonomous vehicle to a location with a lower level of sunlight.
 8. A non-transitory computer readable medium comprising instructions, the instructions, when executed by a computing system, cause the computing system to: receive a level of sunlight based on an amount of light captured by one or more sensors of an autonomous vehicle; map the level of sunlight to a geographical area to generate a map of shaded areas; and based on a temperature within the autonomous vehicle being outside a temperature range, route the autonomous vehicle based on the map of shaded areas to bring the autonomous vehicle within the temperature range.
 9. The non-transitory computer readable medium of claim 8, wherein the one or more sensors are photo receiver sensors that detect ambient light conditions, and wherein the one or more sensors are located behind a windshield of the autonomous vehicle.
 10. The non-transitory computer readable medium of claim 8, wherein the map of shaded areas is generated based on mapping levels of sunlight received from a fleet of autonomous vehicles.
 11. The non-transitory computer readable medium of claim 8, wherein the mapping of the level of sunlight to the geographical area is with respect to time, and the routing of the autonomous vehicle is further based on a current time.
 12. The non-transitory computer readable medium of claim 8, wherein the temperature is received from a temperature sensor located within a cabin of the autonomous vehicle.
 13. The non-transitory computer readable medium of claim 8, the instructions further causing the computing system to: determine that a temperature of a cabin of the autonomous vehicle has fallen below a threshold temperature; and based on the determination, route the autonomous vehicle to a location with a higher level of sunlight.
 14. The non-transitory computer readable medium of claim 8, the instructions further causing the computing system to: determining that a temperature of a cabin of the autonomous vehicle has risen above a threshold temperature; and based on the determination, routing the autonomous vehicle to a location with a lower level of sunlight.
 15. A system comprising: one or more sensors of an autonomous vehicle for capturing a level of sunlight; and a processor for executing instructions stored in memory, wherein execution of the instructions by the processor executes: receiving the level of sunlight captured by the one or more sensors; mapping the level of sunlight to a geographical area to generate a map of shaded areas; and based on a temperature within the autonomous vehicle being outside a temperature range, routing the autonomous vehicle based on the map of shaded areas to bring the autonomous vehicle within the temperature range.
 16. The system of claim 15, wherein the map of shaded areas is generated based on mapping levels of sunlight received from a fleet of autonomous vehicles.
 17. The system of claim 15, wherein the mapping of the level of sunlight to the geographical area is with respect to time, and the routing of the autonomous vehicle is further based on a current time.
 18. The system of claim 15, wherein the temperature is received from a temperature sensor located within a cabin of the autonomous vehicle.
 19. The system of claim 15, wherein execution of the instructions by the processor further executes: determining that a temperature of a cabin of the autonomous vehicle has fallen below a threshold temperature; and based on the determination, routing the autonomous vehicle to a location with a higher level of sunlight.
 20. The system of claim 15, wherein execution of the instructions by the processor further executes: determining that a temperature of a cabin of the autonomous vehicle has risen above a threshold temperature; and based on the determination, routing the autonomous vehicle to a location with a lower level of sunlight. 